An Effective Driver Intention and Trajectory Prediction for Autonomous Vehicle based on LSTM

Fatimetou El Jili

Abstract

In order to make the navigation system of autonomous vehicle more robust and safe in urban environment we propose in this paper a model for driver intention prediction and trajectory prediction. The proposed model is based on LSTM (long short term memory). The model was trained on database of features collected from the driving simulator CARLA. This paper treats four type of intentions, turn left, turn right, go straight and stopping intention. Two cases were treated, the first case is to predict intention before it occurs, the second case corresponds to intention recognition, where the driver already starts maneuvering the intention. Both cases are treated by the same model. The model shows better performances for the second case than the first case with small differences. The main strength of our model is that it gives good performances with a small set of features. The accuracy of the model is 96% for intention prediction and 97% for the intention recognition. The proposed method for trajectory prediction reach an accuracy of 99.9%. Those accuracies are higher than what we found in state of art.

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Paper Citation


in Harvard Style

El Jili F. (2021). An Effective Driver Intention and Trajectory Prediction for Autonomous Vehicle based on LSTM.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1090-1096. DOI: 10.5220/0010321710901096


in Bibtex Style

@conference{icaart21,
author={Fatimetou El Jili},
title={An Effective Driver Intention and Trajectory Prediction for Autonomous Vehicle based on LSTM},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1090-1096},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010321710901096},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - An Effective Driver Intention and Trajectory Prediction for Autonomous Vehicle based on LSTM
SN - 978-989-758-484-8
AU - El Jili F.
PY - 2021
SP - 1090
EP - 1096
DO - 10.5220/0010321710901096